A train control system and method for a train including at least one locomotive or control car and, optionally, at least one railroad car, in a track network having a plurality of tracks, includes at least one computer programmed or configured to receive or determine an indication that the train is stopped, continuously increment a counter in response to the indication that the train is stopped, and control an operator interface to output a current count of the counter to an operator of the train.

This application relates to methods and apparatus for the detection of anomalies in the wheelsets of rail vehicles, for instance for detection of defects such as wheel flats (303) of a wheel (301). The method using a distributed acoustic sensor (106) having a sensing optical fibre (104) deployed along at least part of a rail track (201) as a train (202) moves along that part of the track. The distributed acoustic sensor detects acoustic signals from a plurality of longitudinal sensing portions of the sensing optical fibre. A processor 108 analyses the acoustic signals to determine the train speed v. Having determined the train speed the processor also analyses the acoustic signals for a characteristic acoustic signal so as to detect an anomaly in a wheelset where the characteristic acoustic signal is based on the determined train speed. In particular the method may involve at least a first section of track where the train travels with a first speed v1 and a second section of track where the train travels with a second different speed v2 the characteristic acoustic signal may be a repetitive signal at a frequency that varies proportional to the train speed.

This application relates to methods and apparatus for the detection of anomalies in the wheelsets of rail vehicles, for instance for detection of defects such as wheel flats (303) of a wheel (301). The method using a distributed acoustic sensor (106) having a sensing optical fibre (104) deployed along at least part of a rail track (201) as a train (202) moves along that part of the track. The distributed acoustic sensor detects acoustic signals from a plurality of longitudinal sensing portions of the sensing optical fibre. A processor 108 analyses the acoustic signals to determine the train speed v. Having determined the train speed the processor also analyses the acoustic signals for a characteristic acoustic signal so as to detect an anomaly in a wheelset where the characteristic acoustic signal is based on the determined train speed. In particular the method may involve at least a first section of track where the train travels with a first speed v1 and a second section of track where the train travels with a second different speed v2 the characteristic acoustic signal may be a repetitive signal at a frequency that varies proportional to the train speed.

Systems and methods for detecting train rail and railcar anomalies are disclosed herein. In an example, detecting anomalies includes: receiving measurements from a sensor array coupled to a railcar in a train; obtaining baseline measurements from the sensor array; obtaining, in near real time, measurements from the sensor array while the railcar is operating; and detecting a railcar anomaly based a comparison between the baseline and operating measurements. In an example, the comparison of baseline and operating measurements includes evaluating, over a sequence of time data points, inertia sensor measurements (such as caused by side to side railcar movement) to detect abnormal railcar oscillation. In further examples, the data indexed to a GPS location is stored in a database, and respective alerts are transmitted or outputted to an output device (such as a display device) when an anomaly is detected based on the collected measurements from the railcars.

Systems and methods for detecting train rail and railcar anomalies are disclosed herein. In an example, detecting anomalies includes: receiving measurements from a sensor array coupled to a railcar in a train; obtaining baseline measurements from the sensor array; obtaining, in near real time, measurements from the sensor array while the railcar is operating; and detecting a railcar anomaly based a comparison between the baseline and operating measurements. In an example, the comparison of baseline and operating measurements includes evaluating, over a sequence of time data points, inertia sensor measurements (such as caused by side to side railcar movement) to detect abnormal railcar oscillation. In further examples, the data indexed to a GPS location is stored in a database, and respective alerts are transmitted or outputted to an output device (such as a display device) when an anomaly is detected based on the collected measurements from the railcars.

It is described a system and method to monitor railway wheels, which includes obtaining images of the railway wheel by means of stereo cameras and performing a three-dimensional and two-dimensional model of the railway wheel by means of these images. This two-dimensional model is subjected to analysis of the profile of the railway wheel, comparing it with a two-dimensional model of an ideal wheel and thus measuring the effective wear of its profile. The three-dimensional model is subjected to analysis of the surface of the railway wheel, also comparing it with a three-dimensional model of an ideal wheel and thus measuring the effective wear of its surface.

It is described a system and method to monitor railway wheels, which includes obtaining images of the railway wheel by means of stereo cameras and performing a three-dimensional and two-dimensional model of the railway wheel by means of these images. This two-dimensional model is subjected to analysis of the profile of the railway wheel, comparing it with a two-dimensional model of an ideal wheel and thus measuring the effective wear of its profile. The three-dimensional model is subjected to analysis of the surface of the railway wheel, also comparing it with a three-dimensional model of an ideal wheel and thus measuring the effective wear of its surface.

The present invention discloses an online fault detection device for train wheels, including: base rails 1, working rails 2, guard rails 3, and transducer detectors 4. A section is cut off from each of the symmetrically arranged base rails 1; a section of the working rail 2 transitions into and is attached to an outer side of the base rail 1 for bearing a train wheel; a section of the guard rail 3 transitions into and is attached to a position near an inner side of the base rail 1 for preventing a wheel rim from derailment; transducer detectors 4 and sensors thereof are placed in the space between working rail and guard rail; and the transducer detectors 4 perform automated detection by connecting to a data collection and processing system such as an external computer. With the present invention, good detection effects, low cost, and quick detection of a fault can be achieved.

The present invention discloses an online fault detection device for train wheels, including: base rails 1, working rails 2, guard rails 3, and transducer detectors 4. A section is cut off from each of the symmetrically arranged base rails 1; a section of the working rail 2 transitions into and is attached to an outer side of the base rail 1 for bearing a train wheel; a section of the guard rail 3 transitions into and is attached to a position near an inner side of the base rail 1 for preventing a wheel rim from derailment; transducer detectors 4 and sensors thereof are placed in the space between working rail and guard rail; and the transducer detectors 4 perform automated detection by connecting to a data collection and processing system such as an external computer. With the present invention, good detection effects, low cost, and quick detection of a fault can be achieved.

As a train passes over an array of cameras and lights that have been positioned between the rails of a train system, images are captured of the wheel tread. The number of cameras that will be used in the array may vary depending on the end user of the train system and its specific requirements. Software will capture the images of the portion of each wheel tread and reassemble the images in a linear format. The linear format for each wheel tread will provide an image that can be easily analyzed by the end user of the train system.